A SIR measurement is an important metric of quality performance for digital communication systems. For wireless communication systems such as Third Generation (3G) wireless systems, SIR measurements are used in several link adaptation techniques such as transmit power control and adaptive modulation and coding. Typically, SIR measured at a receiving device is more meaningful than at a transmitting device because SIR measured at a receiving device directly reflects the quality of communicated link signals, especially in the presence of multiple access interference or multipath fading channel.
By definition, a received signal consists of a desired signal and interference. The interference may include other signals and thermal noise at the receiving end. However, the receiving device does not generally have knowledge of either signal power or interference power so that the receiving device needs to perform estimation of both signal and interference power based on received signals using a blind method. A blind method in SIR measurement for a given received signal refers to the signal power and interference power (eventually SIR) obtained only from observation samples of the received signal without any training sequence or any prior knowledge of the desired received signal and interference in the received signal.
There exist several approaches in performing measurement of received SIR. In the prior art, the signal power for a given signal is estimated by averaging the received signal over time, and the interference power is estimated by measuring total power of the received signal and then subtracting the estimated signal power from the total power. The SIR is then determined as the ratio between the estimated signal power and interference power.
The SIR estimation for a given received signal can be performed at different observation points of the receiver structure, such as at the receiver antenna end, at the input to the data demodulator, or at the output from the data demodulator. However, SIR estimates measured at different locations usually have different levels of accuracy because the signal gain or the interference amount at one measurement location is likely to be different from the readings at other locations.
The main problem in measuring the SIR of data signals is that an SIR estimate is likely to deviate from the corresponding true SIR value. Such inaccuracies in SIR estimation arise due to the following two main reasons. First, a signal and its interference cannot be completely separated. Second, desired signals are generally data-modulated, so that the SIR estimation is done in a “blind” way, i.e., without prior knowledge of the data signal. This increases uncertainties in estimating signal power.
In many prior art systems, SIR estimation mainly relies on a mean filter to calculate signal and noise power, resulting in undesirably large bias contribution. Generally, SIR estimation becomes more overestimated as SIR values are smaller, due primarily to a larger bias contribution.
Typically, the k-th demodulated symbol, yk, as an input to a demodulator based SIR estimator, can be represented by:yk=skd+nke  Equation (1)where skd denotes the k-th demodulated desired QPSK signal and nke denotes the total effective interference (including residual intra-cell interference, inter-cell interference and background noise effects), respectively. S refers to the signal and d is the desired signal. The SIR is then estimated in terms of the average signal power, Ps, and the effective interference power, PI, as:
                    SIR        =                                            P              S                                      P              I                                =                                    E              ⁢                              {                                                                                                s                      k                      d                                                                            2                                }                                                    E              ⁢                              {                                                                                                n                      k                      e                                                                            2                                }                                                                        Equation        ⁢                                  ⁢                  (          2          )                    By comparing Equation (2) to the SIR definition used in 3GPP, (i.e., RSCP*SF/Interference), neither RSCP nor ISCP is explicitly evaluated for the measurement. In other words, Equation (2) expresses the SIR measurement of a DPCH more explicitly than the 3GPP definition. In addition, since the SIR measurement is carried out on the data part of the received signal, a blind estimation is required due to the unknown transmit data at the receiving device. The function “E{ }” used herein represents an operator to estimate the statistical average (or expected or mean) value of a variable within the brackets “{ }”. In the context of probability/statistics or communication systems, it is widely conventional to use E{x} to define the average (expected) value of a (random) variable x.
While the SIR definition used in 3GPP is implicitly independent of the data demodulator type used in the receiving device, the SIR measurement in Equation (2) is implemented at the demodulator output. Thus, the SIR given in Equation (2) is likely to be different for different demodulator types. For example, for a given received signal primarily corrupted by interference, the SIR measured at a conventional matched filter receiver is likely to be smaller than that at an advanced receiver, such as an interference canceller, due to reduced interference effects. Note that the SIR at the demodulator output is the primary determinant of communication link performance. However, the SIR measurement on the data portion of the received signal must deal with the unknown transmit data.
FIG. 1 depicts a typical transmitted QPSK signal constellation where Es represents the transmitted QPSK symbol energy. For wireless systems such as 3GPP systems, after spreading the QPSK signal, the resulting spread signal arrives through a radio channel at the receiver. The received signal is then processed by the demodulator, which provides the demodulated symbols, yk for k=1,2, . . . , Nburst, where Nburst is the number of symbols in the data burst of the received signal.
Taking into account the fading channel impact and demodulator gain, in the absence of the effective interference, the typical signal constellation of soft-valued demodulated symbols can be observed on average, as shown in FIG. 2 where Sm represents the m-th demodulated signal symbol.
In the presence of interference, the typical demodulator output symbols can be represented pictorially as in FIG. 3. For a given transmitted symbol, Sk, its output symbol may fall into any point in the QPSK constellation, centering around the associated average demodulated symbol, E{Skd}. In this case, the blind-based average power estimation on the demodulator output would be performed. When the decision for each demodulated symbol is made as to which symbol was sent, some decision error may occur most likely due to the effective interference and fading channel. For example, as shown in FIG. 3, even though S2d was actually sent for the k-th symbol, the interference may cause the demodulator output symbol, marked by yk, to become closer to S1d in the 1st quadrant than the actually transmitted symbol, S2d. As a result, an incorrect decision (i.e., a decision error) on yk may be made. The decision error is the main source of error that causes the average signal power estimate, and consequently the SIR estimate to be overestimated. In lower SIR range (high raw BER range), the average signal power (or SIR) estimate is likely to be more overestimated.
It is therefore desirable to provide a method of performing SIR estimation without experiencing the disadvantages of prior art methods.